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Record W4280589150 · doi:10.2196/35969

Developing Reporting Guidelines for Studies of HIV Drug Resistance Prevalence: Protocol for a Mixed Methods Study

2022· article· en· W4280589150 on OpenAlexaffvenue
Michael Cristian Garcia, Nadia Rehman, Daeria O. Lawson, Pascal Djiadeu, Lawrence Mbuagbaw

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversityImpact
Fundersnot available
KeywordsChecklistFocus groupThematic analysisProtocol (science)UsabilityMedicineData collectionHIV drug resistanceFamily medicineQualitative propertyInformed consentQualitative researchResistance (ecology)PsychologyHuman immunodeficiency virus (HIV)Medical educationAlternative medicineComputer scienceAntiretroviral therapyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: HIV drug resistance is a global health problem that limits the effectiveness of antiretroviral therapy. Adequate surveillance of HIV drug resistance is challenged by heterogenous and inadequate data reporting, which compromises the accuracy, interpretation, and usability of prevalence estimates. Previous research has found that the quality of reporting in studies of HIV drug resistance prevalence is low, and thus better guidance is needed to ensure complete and uniform reporting. OBJECTIVE: This paper contributes to the process of developing reporting guidelines for prevalence studies of HIV drug resistance by reporting the methodology used in creating a reporting item checklist and generating key insights on items that are important to report. METHODS: We will conduct a sequential explanatory mixed methods study among authors and users of studies of HIV drug resistance. The two-phase design will include a cross-sectional electronic survey (quantitative phase) followed by a focus group discussion (qualitative phase). Survey participants will rate the essentiality of various reporting items. This data will be analyzed using content validity ratios to determine the items that will be retained for focus group discussions. Participants in these discussions will revise the items and any additionally suggested items and settle on a complete reporting item checklist. We will also conduct a thematic analysis of the group discussions to identify emergent themes regarding the agreement process. RESULTS: As of November 2021, data collection for both phases of the study is complete. In July 2021, 51 participants had provided informed consent and completed the electronic survey. In October 2021, focus group discussions were held. Nine participants in total participated in two virtual focus group discussions. As of May 2022, data are being analyzed. CONCLUSIONS: This study supports the development of a reporting checklist for studies of HIV drug resistance by achieving agreement among experts on what items should be reported in these studies. The results of this work will be refined and elaborated on by a writing committee of HIV drug resistance experts and external reviewers to develop finalized reporting guidelines. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35969.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.239
metaresearch head score (Gemma)0.231
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.761
Threshold uncertainty score0.938

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2390.231
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0080.009
Science and technology studies0.0070.005
Scholarly communication0.0060.006
Open science0.0050.005
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0810.025

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.720
GPT teacher head0.732
Teacher spread0.013 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainReporting
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes2
Has abstractyes

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